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Create a stored procedure that runs with caller’s rights, including the appropriate "> 5 years" business logic, and grant USAGE on this procedure to ORDER_ADMIN. The ORDER_MANAGER role owns the procedure. 

Create a stored procedure that can be run using both caller’s and owner’s rights (allowing the user to specify which rights are used during execution), and grant USAGE on this procedure to ORDER_ADMIN. The ORDER_MANAGER role owns the procedure. 

Create a stored procedure that runs with owner’s rights, including the appropriate "> 5 years" business logic, and grant USAGE on this procedure to ORDER_ADMIN. The ORDER_MANAGER role owns the procedure.

This scenario would actually not be possible in Snowflake – any user performing a DELETE on a table requires the DELETE privilege to be granted to the role they are using. 

A Snowpipe should be created and configured with AUTO_INGEST = true. A stream should be created to process INSERTS into a single target table using the stream metadata to inform the store number and timestamps.

A stream should be created to accumulate the near real-time data and a task should be created that runs at a frequency that matches the real-time analytics needs. 

An external scheduler should examine the contents of the cloud storage location and issue SnowSQL commands to process the data at a frequency that matches the real-time analytics needs

The copy into command with a task scheduled to run every second should be used to achieve the near-real time requirement. 

1. Create an external table over the JSON data in cloud storage. 2. Create a task that runs every 5 minutes to run a transformation procedure on new data, based on a saved timestamp. 3. Ask the vendor to expose an API so an external function can be used to generate a call to join the data back to the loT data in the transformation procedure. 4. Give the transformed table access to the dashboard tool. 5. Perform the aggregations on the dashboard tool.

1. Create an external table over the JSON data in cloud storage. 2. Create a task that runs every 5 minutes to run a transformation procedure on new data based on a saved timestamp. 3. Ask the vendor to create a data share with the required data that can be imported into the company's Snowflake account. 4. Join the vendor's data back to the loT data using a transformation procedure. 5. Create views over the larger dataset to perform the aggregations required by the dashboard. 6. Give the views access to the dashboard tool.

 privileges to create or manage shares), they can add an Enterprise edition account as a consumer. This feature enables flexibility in data sharing across different Snowflake account editions, facilitating broader data collaboration and accessibility.References: Snowflake's data sharing documentation and the specifics of edition-based capabilities discussed in SnowPro Advanced: Architect certification materials. Question #:47 A company is designing a process for importing a large amount of loT JSON data from cloud storage into Snowflake. New sets of loT data get generated and uploaded approximately every 5 minutes. Once the loT data is in Snowflake, the company needs up-to-date information from an external vendor to join to the data. This data is then presented to users through a dashboard that shows different levels of aggregation. The external vendor is a Snowflake customer. What solution will MINIMIZE complexity and MAXIMIZE performance? 1. Create an external table over the JSON data in cloud storage. 2. Create a task that runs every 5 minutes to run a transformation procedure on new data, based on a saved timestamp. 3. Ask the vendor to expose an API so an external function can be used to generate a call to join the data back to the loT data in the transformation procedure. 4. Give the transformed table access to the dashboard tool. 5. Perform the aggregations on the dashboard tool. 1. Create an external table over the JSON data in cloud storage. 2. Create a task that runs every 5 minutes to run a transformation procedure on new data based on a saved timestamp. 3. Ask the vendor to create a data share with the required data that can be imported into the company's Snowflake account. 4. Join the vendor's data back to the loT data using a transformation procedure. 5. Create views over the larger dataset to perform the aggregations required by the dashboard. 6. Give the views access to the dashboard tool. 1. Create a Snowpipe to bring the JSON data into Snowflake. 2. Use streams and tasks to trigger a transformation procedure when new JSON data arrives. 3. Ask the vendor to expose an API so an external function call can be made to join the vendor's data back to the loT data in a transformation procedure. 4. Create materialized views over the larger dataset to perform the aggregations required by the dashboard. 5. Give the materialized views access to the dashboard tool

1. Create a Snowpipe to bring the JSON data into Snowflake. 2. Use streams and tasks to trigger a transformation procedure when new JSON data arrives. 3. Ask the vendor to create a data share with the required data that is then imported into the Snowflake account. 4. Join the vendor's data back to the loT data in a transformation procedure 5. Create materialized views over the larger dataset to perform the aggregations required by the dashboard. 6. Give the materialized views access to the dashboard tool.

1. Create an external table over the JSON data in cloud storage. 2. Create a task that runs every 5 minutes to run a transformation procedure on new data, based on a saved timestamp. 3. Ask the vendor to expose an API so an external function can be used to generate a call to join the data back to the loT data in the transformation procedure. 4. Give the transformed table access to the dashboard tool. 5. Perform the aggregations on the dashboard tool.

1. Create an external table over the JSON data in cloud storage. 2. Create a task that runs every 5 minutes to run a transformation procedure on new data based on a saved timestamp. 3. Ask the vendor to create a data share with the required data that can be imported into the company's Snowflake account. 4. Join the vendor's data back to the loT data using a transformation procedure. 5. Create views over the larger dataset to perform the aggregations required by the dashboard. 6. Give the views access to the dashboard tool.

 privileges to create or manage shares), they can add an Enterprise edition account as a consumer. This feature enables flexibility in data sharing across different Snowflake account editions, facilitating broader data collaboration and accessibility.References: Snowflake's data sharing documentation and the specifics of edition-based capabilities discussed in SnowPro Advanced: Architect certification materials. Question #:47 A company is designing a process for importing a large amount of loT JSON data from cloud storage into Snowflake. New sets of loT data get generated and uploaded approximately every 5 minutes. Once the loT data is in Snowflake, the company needs up-to-date information from an external vendor to join to the data. This data is then presented to users through a dashboard that shows different levels of aggregation. The external vendor is a Snowflake customer. What solution will MINIMIZE complexity and MAXIMIZE performance? 1. Create an external table over the JSON data in cloud storage. 2. Create a task that runs every 5 minutes to run a transformation procedure on new data, based on a saved timestamp. 3. Ask the vendor to expose an API so an external function can be used to generate a call to join the data back to the loT data in the transformation procedure. 4. Give the transformed table access to the dashboard tool. 5. Perform the aggregations on the dashboard tool. 1. Create an external table over the JSON data in cloud storage. 2. Create a task that runs every 5 minutes to run a transformation procedure on new data based on a saved timestamp. 3. Ask the vendor to create a data share with the required data that can be imported into the company's Snowflake account. 4. Join the vendor's data back to the loT data using a transformation procedure. 5. Create views over the larger dataset to perform the aggregations required by the dashboard. 6. Give the views access to the dashboard tool. 1. Create a Snowpipe to bring the JSON data into Snowflake. 2. Use streams and tasks to trigger a transformation procedure when new JSON data arrives. 3. Ask the vendor to expose an API so an external function call can be made to join the vendor's data back to the loT data in a transformation procedure. 4. Create materialized views over the larger dataset to perform the aggregations required by the dashboard. 5. Give the materialized views access to the dashboard tool

1. Create a Snowpipe to bring the JSON data into Snowflake. 2. Use streams and tasks to trigger a transformation procedure when new JSON data arrives. 3. Ask the vendor to create a data share with the required data that is then imported into the Snowflake account. 4. Join the vendor's data back to the loT data in a transformation procedure 5. Create materialized views over the larger dataset to perform the aggregations required by the dashboard. 6. Give the materialized views access to the dashboard tool.